Biology subjects

Llabres Segura, M.

Publications and source records attributed to Llabres Segura, M..

2 recordsLinked to original sources

bioRxiv2024

MetaDAG: a web tool to generate and analysemetabolic networks

We introduce MetaDAG, a web-based tool designed for metabolic network reconstruction and analysis. MetaDAG is capable of constructing metabolic networks associated with specific organisms, sets of organisms, sets of reactions, sets of enzymes, and sets of KO (KEGG Orthology) identifiers. To generate these metabolic networks, MetaDAG retrieves from the KEGG database the chemical reaction information that corresponds to the users queries. MetaDAG computes a reaction graph as a first metabolic graph model. This reaction graph is a network in which nodes represent reactions, and edges between reactions indicate the presence of a metabolite produced by one reaction and consumed by another. Next, as a second metabolic model, MetaDAG computes a directed acyclic graph called a metabolic DAG (m-DAG for short). The m-DAG is obtained from the reaction graph by collapsing all strongly connected components into single nodes. As a result, the m-DAG representation reduces considerably the number of nodes while keeping and also highlighting the networks connectivity. Both metabolic models, the reaction graph, and the m-DAGs, are displayed on an interactive web page to assist the users in visualising and analysing the networks. Furthermore, users can retrieve the nodes information linked to the KEGG database. All generated files, including images containing metabolic network information and analysis results, are available for download directly from the web page. In the Eukariotes test presented here, MetaDAG has demonstrated its effectiveness in classifying all eukaryotes from the KEGG database at both the kingdom and phyla taxonomy levels.

bioinformatics↗

bioRxiv2022

Prots2Net: a PPIN predictor of a proteome or a metaproteome sample

MotivationAll molecular functions and biological processes are carried out by groups of proteins that interact to each other. Proteins interactions are modeled by simple networks called Protein-Protein Interaction Networks (PPINs) whose nodes are proteins and whose edges are the protein-protein interactions. PPINs are broadly accepted to model the proteins functional relations, and their analysis has become a key ingredient in the study of protein functions. New proteins are collected every day from metaproteomic data, and their functional relations must be obtained with high-throughput technology. Retrieving protein-protein interaction data experimentally is a very high time-consuming and labor-intensive task. Consequently, in the last years, the biological community is looking for computational methods to correctly predict PPIs. ResultsWe present here Prots2Net, a tool designed to predict the PPIs of a proteome or a metaproteome sample. Our prediction model is a multilayer perceptron neural network that uses protein sequence information only from the input proteins and interaction information from the STRING database. To train the model, Prots2Net explores the PPIs retrieved from the STRING database of two selected species. The tests, reported here on the Yeast and the Human datasets, show that Prots2Net performs better than the previous prediction methods that used protein sequence information only. Therefore, considering the information of PPI data available on the STRING database improves the PPI prediction. Availabilityhttps://github.com/adriaalcala/prots2net Contactmerce.llabres@uib.es

bioinformatics↗